Velocity in relation to flow
str(TotalData)
'data.frame': 1725905 obs. of 26 variables:
$ Date : chr "20191118" "20191118" "20191118" "20191118" ...
$ File.name : chr "20191118_view1_" "20191118_view1_" "20191118_view1_" "20191118_view1_" ...
$ X : num 0.319 NaN 0.318 NaN 0.343 ...
$ Y : num NaN NaN NaN NaN 0.0999 ...
$ Z : num NaN -0.0573 NaN -0.0567 -0.055 ...
$ Track : int 1 1 1 1 1 1 1 1 1 1 ...
$ View : chr "1_" "1_" "1_" "1_" ...
$ D_V_T : Factor w/ 153 levels "20191118_1__1",..: 1 1 1 1 1 1 1 1 1 1 ...
$ D_V : Factor w/ 35 levels "20191118_1_",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Flow.rate : Factor w/ 5 levels "0","0.6","3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Chlorophyll : Factor w/ 7 levels "0","4.3","4.6",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Guano : chr "Absent" "Absent" "Absent" "Absent" ...
$ Light : chr "Present" "Present" "Present" "Present" ...
$ dx : num NaN NaN NaN NaN -0.00115 ...
$ dy : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ dz : num NaN NaN NaN 0.00168 NaN ...
$ d : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ vx : num NaN NaN NaN NaN -0.0345 ...
$ vy : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ vz : num NaN NaN NaN 0.0503 NaN ...
$ v : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ heading : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ pitch : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ turn.anglexy: num NaN NaN NaN NaN 1.29 ...
$ turn.angleyz: num NaN NaN NaN NaN 2.07 ...
$ turn.angle : num NA NaN NaN NaN NaN NaN NaN NaN NaN NaN ...TotalData$Flow.rate <- as.character(TotalData$Flow.rate)
TotalData$Flow.rate <- as.numeric(TotalData$Flow.rate)
head(TotalData) ## all TotalData rowsstr(TotalData)'data.frame': 1725905 obs. of 26 variables:
$ Date : chr "20191118" "20191118" "20191118" "20191118" ...
$ File.name : chr "20191118_view1_" "20191118_view1_" "20191118_view1_" "20191118_view1_" ...
$ X : num 0.319 NaN 0.318 NaN 0.343 ...
$ Y : num NaN NaN NaN NaN 0.0999 ...
$ Z : num NaN -0.0573 NaN -0.0567 -0.055 ...
$ Track : int 1 1 1 1 1 1 1 1 1 1 ...
$ View : chr "1_" "1_" "1_" "1_" ...
$ D_V_T : Factor w/ 153 levels "20191118_1__1",..: 1 1 1 1 1 1 1 1 1 1 ...
$ D_V : Factor w/ 35 levels "20191118_1_",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Flow.rate : num 0 0 0 0 0 0 0 0 0 0 ...
$ Chlorophyll : Factor w/ 7 levels "0","4.3","4.6",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Guano : chr "Absent" "Absent" "Absent" "Absent" ...
$ Light : chr "Present" "Present" "Present" "Present" ...
$ dx : num NaN NaN NaN NaN -0.00115 ...
$ dy : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ dz : num NaN NaN NaN 0.00168 NaN ...
$ d : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ vx : num NaN NaN NaN NaN -0.0345 ...
$ vy : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ vz : num NaN NaN NaN 0.0503 NaN ...
$ v : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ heading : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ pitch : num NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
$ turn.anglexy: num NaN NaN NaN NaN 1.29 ...
$ turn.angleyz: num NaN NaN NaN NaN 2.07 ...
$ turn.angle : num NA NaN NaN NaN NaN NaN NaN NaN NaN NaN ...TotalData$vel.flow <- TotalData$Flow.rate+TotalData$vy
TotalData$Flow.rate <- as.factor(TotalData$Flow.rate)
##remake complete cases dataframe with vel.flow included
CC.TotalData <- na.omit(TotalData)
head(CC.TotalData) ##only complete cases of TotalData str(CC.TotalData)'data.frame': 1112228 obs. of 27 variables:
$ Date : chr "20191118" "20191118" "20191118" "20191118" ...
$ File.name : chr "20191118_view1_" "20191118_view1_" "20191118_view1_" "20191118_view1_" ...
$ X : num 0.327 0.163 0.18 0.167 0.158 ...
$ Y : num 0.0717 0.0405 0.0373 0.0891 0.113 ...
$ Z : num -0.0595 -0.0774 -0.0835 -0.1104 -0.0953 ...
$ Track : int 1 1 1 1 1 1 1 1 1 1 ...
$ View : chr "1_" "1_" "1_" "1_" ...
$ D_V_T : Factor w/ 153 levels "20191118_1__1",..: 1 1 1 1 1 1 1 1 1 1 ...
$ D_V : Factor w/ 35 levels "20191118_1_",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Flow.rate : Factor w/ 5 levels "0","0.6","3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Chlorophyll : Factor w/ 7 levels "0","4.3","4.6",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Guano : chr "Absent" "Absent" "Absent" "Absent" ...
$ Light : chr "Present" "Present" "Present" "Present" ...
$ dx : num -0.00115 0.00283 0.00282 0 -0.00289 ...
$ dy : num -0.00196 -0.00183 0.00505 0.00511 0.00535 ...
$ dz : num -0.00112 0.00056 -0.00112 0.00112 0.00168 ...
$ d : num 0.00253 0.00342 0.00589 0.00523 0.00631 ...
$ vx : num -0.0345 0.0849 0.0846 0 -0.0867 ...
$ vy : num -0.0587 -0.0548 0.1514 0.1533 0.1605 ...
$ vz : num -0.0335 0.0168 -0.0335 0.0336 0.0503 ...
$ v : num 0.0759 0.1025 0.1767 0.157 0.1892 ...
$ heading : num 0.531 -0.997 0.509 0 -0.495 ...
$ pitch : num -0.458 0.165 -0.191 0.216 0.269 ...
$ turn.anglexy: num 1.36 1.33 1.37 1.08 0.95 ...
$ turn.angleyz: num 2.26 2.66 2.72 2.46 2.27 ...
$ turn.angle : num 12.1 31.1 59.3 26.6 16.7 ...
$ vel.flow : num -0.0587 -0.0548 0.1514 0.1533 0.1605 ...
- attr(*, "na.action")= 'omit' Named int [1:613677] 1 2 3 4 5 6 7 8 9 10 ...
..- attr(*, "names")= chr [1:613677] "65" "67" "70" "71" ...```r
plot(CC.TotalData$Chlorophyll, log10(CC.TotalData$vel.flow), xlab = \Chlorophyll (mg/L)\, ylab = \Velocity in relation to flow (Log^10Warning in is.factor(y) : NaNs produced
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 1 is not drawn
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 5 is not drawn
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 7 is not drawn
```r
plot(CC.TotalData$Flow.rate, log10(CC.TotalData$vel.flow), xlab = \Flow Rate (cm/s)\, ylab = \Velocity in relation to flow (Log^10Warning in is.factor(y) : NaNs produced
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 1 is not drawn
```r
plot(CC.TotalData$Guano, log10(CC.TotalData$vel.flow), xlab = \Guano\, ylab = \Velocity in relation to flow (Log^10Warning in is.factor(y) : NaNs produced
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 1 is not drawn
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 2 is not drawn
```r
plot(CC.TotalData$Light, log10(CC.TotalData$vel.flow), xlab = \Light\, ylab = \Velocity in relation to flow (Log^10Warning in is.factor(y) : NaNs produced
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 1 is not drawn
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :
Outlier (-Inf) in boxplot 2 is not drawn
save.image("~/Post-doc/Data/Total Merged Data File (April 5 2022).RData")
---
title: "Velocity in relation to flow"
output: html_notebook
---


```{r}
rm(list=ls(all=TRUE))
load("C:\\Users\\Nicole Hellessey\\Documents\\Post-doc\\Data\\Total Merged Data File (April 5 2022).Rdata")


## Flow happens in the Y direction

## So, flow + vy = vel.flow (velocity in relation to flow)


## Need to make flow rate numeric first
str(TotalData)
TotalData$Flow.rate <- as.character(TotalData$Flow.rate)
TotalData$Flow.rate <- as.numeric(TotalData$Flow.rate)

head(TotalData) ## all TotalData rows
str(TotalData)
TotalData$vel.flow <- TotalData$Flow.rate+TotalData$vy
TotalData$Flow.rate <- as.factor(TotalData$Flow.rate)


##remake complete cases dataframe with vel.flow included
CC.TotalData <- na.omit(TotalData)
head(CC.TotalData) ##only complete cases of TotalData 
str(CC.TotalData)


## Basic plot of vy vs Flow Rate
plot(CC.TotalData$Flow.rate, CC.TotalData$vy, xlab = "Flow Rate (cm/s)", ylab = "Velocity in Y direction (vy, cm/s)")

## Basic plot of vel.flow vs Flow Rate (should be different to above)
plot(CC.TotalData$Flow.rate, CC.TotalData$vel.flow, xlab = "Flow Rate (cm/s)", ylab = "Velocity in Y direction minus flow (vel.flow, cm/s)")


## Plotting turn angles in relation to swim velocity (accounting for flow rate) for every individual
for (i in 1:length(ind)){
jpeg(filename=paste(ind[i], '.jpeg', sep = ''), width = 480, height = 480)
plot(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]], CC.TotalData$vel.flow[CC.TotalData$D_V_T==ind[i]],
            xlab = "Turn Angles",
     ylab = "Velocity in relation to flow (cm/s)",
          main = ind[i]) 
dev.off()
}

library(ggplot2)

## plot Flow Rate by Vel.flow filled by Chla faceted by light
ggplot(CC.TotalData,aes(x=Flow.rate, y=log10(vel.flow), fill=Chlorophyll))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Flow Rate (cm/s)", y = "Velocity in relation to flow (Log transformed)(cm/s)", title = "Light") +
  scale_fill_manual(values=c("greenyellow", "yellowgreen","lightgreen", "green", "green3", "green4", "darkgreen"),name = "Chlorophyll (mg/L)",
                    labels=c("0", "4.3", "4.6", "6.1", "7.6", "13.5", "19"))+
  facet_grid(~Light, scales = "free_x", space = "free")

## plot Chlorophyll by vel.flow filled by flow rate faceted by light
ggplot(CC.TotalData,aes(x=Chlorophyll, y=vel.flow, fill=Flow.rate))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Chlorophyll (mg/L)", y = "Velocity in relation to flow (cm/s)", title = "Light") +
  scale_fill_manual(values=c("white", "beige", "yellow", "orange", "red"), name = "Flow Rate (cm/s)",
                    labels=c("0", "0.6", "3", "5.9", "8"))+
  facet_grid(~Light, scales = "free_x", space = "free")

## plot Chlorophyll by turn angle filled by flow rate facted by light
ggplot(CC.TotalData,aes(x=Chlorophyll, y=turn.angle, fill=Flow.rate))+
  geom_boxplot(notch=F, notchwidth=0.3,outlier.shape=1,outlier.size=2, coef=1.5)+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  labs(size= "",x = "Chlorophyll (mg/L)", y = "Turn angle (degrees)", title = "Light") +
  scale_fill_manual(values=c("white", "beige", "yellow", "orange", "red"), name = "Flow Rate (cm/s)",
                    labels=c("0", "0.6", "3", "5.9", "8"))+
  facet_grid(~Light, scales = "free_x", space = "free")

## plot turn angle by vel.flow filled by chla, faceted by flow rate
ggplot(CC.TotalData,aes(x=turn.angle, y=vel.flow))+
  geom_point(aes(colour=Chlorophyll))+
  facet_grid(~Flow.rate, scales = "free_x", space = "free")+
  labs(size= "",x = "Turn angle (degrees)", y = "Velocity in relation to flow (cm/s)", title = "Flow Rate (cm/s)")+
  theme(axis.text=element_text(color="black"))+
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))+
  theme(panel.grid.minor=element_blank())+
  scale_colour_manual(values=c("greenyellow", "yellowgreen","lightgreen", "green", "green3", "green4", "darkgreen"),name = "Chlorophyll (mg/L)", labels=c("0", "4.3", "4.6", "6.1", "7.6", "13.5", "19"))
 
```


```{r}
## xy plot of means and variance of swimming speed and chlorophyll and turn angle, heading relative to flow
## still need to make Guano and Light factors in tab_AGG  ############################################################################

    ######## velocity means and SD  ######
plot(tab_AGG$Chlorophyll, tab_AGG$mean.velocity, xlab = "Chlorophyll (mg/L)", ylab = "Velocity (Aggregate mean, mm/s)")
plot(tab_AGG$Chlorophyll, tab_AGG$sd.velocity, xlab = "Chlorophyll (mg/L)", ylab = "Velocity (Aggregate SD, mm/s)")
plot(tab_AGG$Flow.Rate, tab_AGG$mean.velocity, xlab = "Flow Rate (cm/s)", ylab = "Velocity (Aggregate mean, mm/s)")
plot(tab_AGG$Flow.Rate, tab_AGG$sd.velocity, xlab = "Flow Rate (cm/s)", ylab = "Velocity (Aggregate SD, mm/s)")
#plot(tab_AGG$Guano, tab_AGG$mean.velocity, xlab = "Guano", ylab = "Velocity (Aggregate mean, mm/s)")           ################# Guano and Light still not factors 
#plot(tab_AGG$Guano, tab_AGG$sd.velocity, xlab = "Guano", ylab = "Velocity (Aggregate SD, mm/s)")
#plot(tab_AGG$Light, tab_AGG$mean.velocity, xlab = "Light", ylab = "Velocity (Aggregate mean, mm/s)")           ################# Guano and Light still not factors 
#plot(tab_AGG$Light, tab_AGG$sd.velocity, xlab = "Light", ylab = "Velocity (Aggregate SD, mm/s)")

    ######## turn angle means and SD  ######
plot(tab_AGG$Chlorophyll, tab_AGG$turn.angle, xlab = "Chlorophyll (mg/L)", ylab = "Turn Angle (degrees)")
plot(tab_AGG$Flow.Rate, tab_AGG$turn.angle, xlab = "Flow Rate (cm/s)", ylab = "Turn Angle (degrees)")
#plot(tab_AGG$Guano, tab_AGG$turn.angle, xlab = "Guano", ylab = "Turn Angle (degrees)")           ################# Guano and Light still not factors 
#plot(tab_AGG$Light, tab_AGG$turn.angle, xlab = "Light", ylab = "Turn Angle (degrees)")

    ######## heading means and SD  ######
plot(tab_AGG$Chlorophyll, tab_AGG$heading/(2*pi)*360, xlab = "Chlorophyll (mg/L)", ylab = "Heading (degrees)")
plot(tab_AGG$Flow.Rate, tab_AGG$heading/(2*pi)*360, xlab = "Flow Rate (cm/s)", ylab = "Heading (degrees)")
#plot(tab_AGG$Guano, tab_AGG$heading, xlab = "Guano", ylab = "Heading (degrees)")           ################# Guano and Light still not factors 
#plot(tab_AGG$Light, tab_AGG$heading, xlab = "Light", ylab = "Heading (degrees)") 

## plus mean of total tracks for turn angle and heading, and pathwise velocity
str(CC.TotalData)
CC.TotalData$Guano <- as.factor(CC.TotalData$Guano)
CC.TotalData$Light <- as.factor(CC.TotalData$Light)

## aggregate distribution of all angles and headings, not just pathwise
plot(CC.TotalData$Chlorophyll, log10(CC.TotalData$v), xlab = "Chlorophyll (mg/L)", ylab = "Velocity (Log^10, mm/s)")
plot(CC.TotalData$Flow.rate, log10(CC.TotalData$v), xlab = "Flow Rate (cm/s)", ylab = "Velocity (Log^10, mm/s)")
plot(CC.TotalData$Guano, log10(CC.TotalData$v), xlab = "Guano", ylab = "Velocity (Log^10, mm/s)")           
plot(CC.TotalData$Light, log10(CC.TotalData$v), xlab = "Light", ylab = "Velocity (Log^10, mm/s)")           

     ###### velocity in relation to flow, aggregates of total data  ######
plot(CC.TotalData$Chlorophyll, log10(CC.TotalData$vel.flow), xlab = "Chlorophyll (mg/L)", ylab = "Velocity  in relation to flow (Log^10, mm/s)")
plot(CC.TotalData$Flow.rate, log10(CC.TotalData$vel.flow), xlab = "Flow Rate (cm/s)", ylab = "Velocity  in relation to flow (Log^10, mm/s)")
plot(CC.TotalData$Guano, log10(CC.TotalData$vel.flow), xlab = "Guano", ylab = "Velocity in relation to flow (Log^10, mm/s)")     
plot(CC.TotalData$Light, log10(CC.TotalData$vel.flow), xlab = "Light", ylab = "Velocity in relation to flow (Log^10, mm/s)")  

    ######## turn angle means and SD  ######
plot(CC.TotalData$Chlorophyll, CC.TotalData$turn.angle, xlab = "Chlorophyll (mg/L)", ylab = "Turn Angle (degrees)")
plot(CC.TotalData$Flow.rate, CC.TotalData$turn.angle, xlab = "Flow Rate (cm/s)", ylab = "Turn Angle (degrees)")
plot(CC.TotalData$Guano, CC.TotalData$turn.angle, xlab = "Guano", ylab = "Turn Angle (degrees)")
plot(CC.TotalData$Light, CC.TotalData$turn.angle, xlab = "Light", ylab = "Turn Angle (degrees)")

    ######## heading means and SD  ######
plot(CC.TotalData$Chlorophyll, CC.TotalData$heading/(2*pi)*360, xlab = "Chlorophyll (mg/L)", ylab = "Heading (degrees)")
plot(CC.TotalData$Flow.rate, CC.TotalData$heading/(2*pi)*360, xlab = "Flow Rate (cm/s)", ylab = "Heading (degrees)")
plot(CC.TotalData$Guano, CC.TotalData$heading/(2*pi)*360, xlab = "Guano", ylab = "Heading (degrees)")         
plot(CC.TotalData$Light, CC.TotalData$heading/(2*pi)*360, xlab = "Light", ylab = "Heading (degrees)") 

```


```{r}
save.image("~/Post-doc/Data/Total Merged Data File (April 5 2022).RData")


```

